Potential Sources Of Error Research Articles

A non-invasive heart rate prediction method using a convolutional approach.

The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of physiological signals, specifically photoplethysmogram (PPG) data which is a valuable tool for non-invasive heart rate prediction. Recognizing the global challenge of heart failure, the study seeks to provide a rapid, accurate, and non-invasive alternative to traditional, uncomfortable blood pressure cuffs. To achieve more accurate and efficient heart rate estimates, a k-fold CNN model with an optimal number of convolutional layers is employed. While the findings show promising results, the study addresses potential sources of error in cuffless PPG-based heart rate measurement, including motion artifacts and skin color variations, emphasizing the need for validation against gold standard methods. The research optimizes a CNN model with optimal layers, operating on 1D arrays of 8-s data slices and employing k-fold cross-validation to mitigate probabilistic uncertainties. Finally, the model yields a remarkable minimum absolute error (MAE) rate of 6.98 beats per minute (bpm), marking a significant 10% improvement over recent studies. The study also advances medical diagnostics and data analysis, then lays a strong foundation for developing cost-effective, reliable devices that offer a more comfortable and efficient way of predicting heart rate.

Irradiance modeling of tubular ultraviolet light bulbs

Clean indoor air is crucial for human lives in residential and workplace settings, including food safety and supply chains. Since the COVID-19 outbreak, disinfecting air has become vital for the public as the SARS-CoV-2 virus and other infectious diseases transmit via inhalable aerosols. Ultraviolet (UV) light is known to be effective in disinfecting air. However, it is still challenging to properly design practical UV applications with common light design software. Visible light analysis software, AGi32, assists architectural applications. AGi32 utilizes digitized luminaire IES files to model light intensity on user-designed geometry in common far-field uses. IES files are based on far-field photometry to model light intensity at different distances and angles from the source. The common application of IES files is to model visible light intensities; however, IES files generated for UV light are still rarely available. Due to the increasing need for surface and air disinfection, modeling UV light intensity using IES files may be beneficial for designing and evaluating systems containing UV light bulbs in near-field applications. There is limited information regarding the accuracy of UV modeling in AGi32 software using IES files. Therefore, the research objectives were to (1) create IES files of a UV-C germicidal lamp (254 nm) and a visible fluorescent lamp with almost identical metrics; (2) compare the IES files output with physical UV measurements inside and outside of an air duct; (3) develop correlations between light intensities of visible and UV light bulbs. Linear correlations were observed when comparing UV irradiance and visible illuminance for both measured and modeled data. The results indicated high variability between the measured and modeled light data, signifying the importance of further investigation of potential error sources and improving the accuracy of modeling.

Sensitivity of wavelet-based optical flow velocimetry (wOFV) to common experimental error sources

Abstract The influence of several potential error sources and non-ideal experimental effects on the accuracy of a wavelet-based optical flow velocimetry (wOFV) method when applied to tracer particle images is evaluated using data from a series of synthetic flows. Out-of-plane particle displacements, severe image noise, laser sheet thickness reduction, and image intensity non-uniformity are shown to decrease the accuracy of wOFV in a similar manner to correlation-based particle image velocimetry (PIV). For the error sources tested, wOFV displays a similar or slightly increased sensitivity compared to PIV, but the wOFV results are still more accurate than PIV when the magnitude of the non-ideal effects remain within expected experimental bounds. For the majority of test cases, the results are significantly improved by using image pre-processing filters and the magnitude of improvement is consistent between wOFV and PIV. Flow divergence does not appear to have an appreciable effect on the accuracy of wOFV velocity estimation, even though the underlying fluid transport equation on which wOFV is based implicitly assumes that the motion is divergence-free. This is a significant finding for the broader applicability of planar velocimetry measurements using wOFV. Finally, it is noted that the accuracy of wOFV is not reduced notably in regions of the image between tracer particles, as long as the overall seeding density is not too sparse i.e. below 0.02 particles per pixel. This explicitly demonstrates that wOFV (when applied to particle images) yields an accurate whole field measurement, and not only at or adjacent to the discrete particle locations.

Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions

Abstract. Urban air quality is an important part of human well-being, and its detailed and precise modeling is important for efficient urban planning. In this study the potential sources of errors in large eddy simulation (LES) runs of the PALM model in stable conditions for a high-traffic residential area in Prague, Czech Republic, with a focus on street canyon ventilation, are investigated. The evaluation of the PALM model simulations against observations obtained during a dedicated campaign revealed unrealistically high concentrations of modeled air pollutants for a short period during a winter inversion episode. To identify potential reasons, the sensitivities of the model to changes in meteorological boundary conditions and adjustments of model parameters were tested. The model adaptations included adding the anthropogenic heat from cars, setting a bottom limit of the subgrid-scale turbulent kinetic energy (TKE), adjusting the profiles of parameters of the synthetic turbulence generator in PALM, and limiting the model time step. The study confirmed the crucial role of the correct meteorological boundary conditions for realistic air quality modeling during stable conditions. Besides this, the studied adjustments of the model parameters proved to have a significant impact in these stable conditions, resulting in a decrease in concentration overestimation in the range 30 %–66 % while exhibiting a negligible influence on model results during the rest of the episode. This suggested that the inclusion or improvement of these processes in PALM is desirable despite their negligible impact in most other conditions. Moreover, the time step limitation test revealed numerical inaccuracies caused by discretization errors which occurred during such extremely stable conditions.

Bayesian finite element model inversion of offshore wind turbine structures for joint parameter-load estimation

Operating in harsh and unsteady marine environment, offshore wind turbine (OWT) structures are exposed to unpredictable wind and wave loads. Identifying the structural loads and their effects on the OWTs allow for predicting the remaining fatigue life of these structures and improving the structural design procedure. In this paper, a finite element (FE) model inversion method is presented to estimate the unknown loads and model parameters of OWTs using sparse measurement data. A realistic FE model of an OWT structure with jacket substructure is created in the open-source simulation platform, OpenSees. A Bayesian inference framework is presented to integrate the measured data with the FE model to estimate unknown wind loads and mass of rotor-nacelle assembly. To evaluate the performance of this data assimilation framework, the effect of sensor type, number of sensors, and modeling errors on the estimation accuracy of wind loads and model parameters are investigated through different case studies where synthetic data are used as measurements. The results of this study are important to guide instrumentation of new OWT structures, and to understand the potential limitations and sources of error in the real-world application of this data assimilation framework for joint model parameter and input load estimation.

Comparison of Martin/Hopkins, Sampson/NIH, and Friedewald Equations for the Estimation of Low-Density Lipoprotein Cholesterol and its Effect on Treatment Thresholds for Patients at a Large Academic Medical Center

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is routinely used in the clinical management of patients at risk for cardiovascular disease (CVD). Currently, LDL-C is frequently calculated with results from the standard lipid panel (total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), and triglycerides (TG)); the Friedewald equation is one commonly utilized calculation for LDL-C (FLDL-C). Despite its well-documented limitations, including incorrect estimation of LDL-C with high TGs, the Friedewald equation is still used by many clinical laboratories where patients having TG levels >400 mg/dL are reflexed to direct LDL (DLDL-C) measurement. Recent guidelines recommend the use of “modern” equations such as Martin/Hopkins (MLDL-C) and Sampson/NIH (SLDL-C) equations which account for variations in TG and non-HDL-C. In this study, we assessed the accuracy of the Martin/Hopkins and Sampson/NIH equations for estimating LDL-C levels up to 800 mg/dL in our patient populations and the impact of the equations to patients’ reclassification. As a comparison with previous studies, the misclassification of patients with dyslipidemia into incorrect LDL-C based treatment groups (<70, 70-99, 100-189, and >= 190 mg/dL) is calculated. Methods Lipid panel and DLDL-C results are obtained using Siemens Advia and Atellica chemistry analyzers. Data analysis including calculation of mean absolute difference (MAD) between DLDL-C and calculated LDL-C and analysis of treatment group misclassification for TG < 400 and 400 – 800 mg/dL is performed using Python 3.11.5, Pandas 2.1.4, and Scipy 1.11.1. Results 9,306 out of 296,362 lipid panels from 2021 to 2023 have either corresponding provider ordered (DLDL-C) testing or reflex DLDL-C testing performed. For all TG values, MAD (mg/dL)/total treatment group misclassification (%) is: FLDL-C 42.14/50.66, MLDL-C 21.09/30.03, and SLDL-C 25.51/40.22. For TG values < 400 mg/dL: FLDL-C 19.33/32.21, MDLD-C 16.67/27.84, and SLDL-C 16.73/28.20 with a sample size of 3,614. For TG values 400 - 800 mg/dL: FLDL-C 47.66/65.25, MLDL-C 20.75/30.48, and SLDL-C 31.36/48.8 with a sample size of 4,855. In particular, the Martin/Hopkins vs. Sampson/NIH vs. Friedewald treatment group misclassifications (%) for TG 400 – 800 mg/dL are: DLDL < 70 mg/dL 6.34/1.67/0.60, DLDL 70 – 99 mg/dL 9.31/14.91 /20.62, DLDL 100 – 189 mg/dL 10.53/26.26/38.19, DLDL >= 190 mg/dL 4.30/5.95/5.58. Conclusion In terms of misclassification rates across LDL-C based treatment groups used in prior studies, the Martin/Hopkins equation performs better overall than the current Friedewald equation and gives similar overall rates of misclassification (27.84 vs. 30.48%) between the TG < 400 mg/dL and the TG 400 – 800 mg/dL groups, allowing the possibility to reflex to direct LDL-C testing only at the higher threshold of TG 800 mg/dL, reducing reflexed DLDL tests by over 100 per month at our institution. A potential source of error is chemistry analyzer DLDL testing rather than the gold standard LDL-C β-quantification used in previous studies.

Identifying and Addressing Errors in Anatomical Pathology: A Survey Analysis

Abstract Introduction/Objective To get an update on potential sources of pre-analytical, analytical, post-analytical and other sources of errors in anatomic pathology and strategies to mitigate such errors. Methods/Case Report The authors designed a 5-question survey in Google software and shared it widely on social media platforms. Results (if a Case Study enter NA) The survey, which garnered 43 responses from staff across various areas of the anatomic pathology laboratory, revealed significant insights. Labeling and fixation/preservation errors emerged as the most prevalent pre-analytical errors, while discrepancies between gross and microscopic examinations were common analytical errors. Specimen handling, inadequate clinical information, and transportation were the most frequently perceived other potential sources of errors. The implications of these findings are clear: robust protocols with multiple identifiers, staff training, and barcode labeling can significantly reduce patient identification errors. Similarly, standardized collection instructions, pre-labeled containers, and meticulous verification processes can effectively minimize such errors. Validated tests, standardized scoring systems, and proper investigation of quality control discrepancies are key to reducing analytical errors. Documented procedures, staff training, regular equipment calibration, and reagent testing are also crucial. Conclusion This study contributes to the literature on the current state of potential sources of errors in anatomic pathology. The study findings and recommendations on strategies to address such errors would improve the performance of the anatomic pathology laboratory and eventually improve patient care.

SLIM-assisted automatic cartography of cell death types and rates resulting from localized photodynamic treatment

We report a spatial light interference microscopy (SLIM)-based methodology aimed at automatic monitoring and analysis of changes in cellular morphology within extended fields of view in cytological samples. The experimental validation was performed on HeLa cells in vitro subjected to localized photodynamic treatment. The performed long-term noninvasive monitoring using the SLIM technique allowed us to estimate quantitative parameters characterizing the dynamics of average phase shift in individual cells and to reveal changes in their morphology specific for different mechanisms of cell death. The results obtained evidenced that the proposed SLIM-based methodology provides an opportunity for identification of cell death type and quantification of cell death rate in an automatic mode. The major sources of potential errors that can affect the results obtained are discussed. The developed methodology is promising for automatic monitoring of large ensembles of individual cells and for quantitative characterization of their response to various treatment modalities.

Assessing Large Language Models Used for Extracting Table Information from Annual Financial Reports

The extraction of data from tables in PDF documents has been a longstanding challenge in the field of data processing and analysis. While traditional methods have been explored in depth, the rise of Large Language Models (LLMs) offers new possibilities. This article addresses the knowledge gaps regarding LLMs, specifically ChatGPT-4 and BARD, for extracting and interpreting data from financial tables in PDF format. This research is motivated by the real-world need to efficiently gather and analyze corporate financial information. The hypothesis is that LLMs—in this case, ChatGPT-4 and BARD—can accurately extract key financial data, such as balance sheets and income statements. The methodology involves selecting representative pages from 46 annual reports of large Swiss corporations listed in the SMI Expanded Index from 2022 and copy–pasting text from these into LLMs. Eight analytical questions were posed to the LLMs, and their responses were assessed for accuracy and for identifying potential error sources in data extraction. The findings revealed significant variance in the performance of ChatGPT-4 and another LLM, BARD, with ChatGPT-4 generally exhibiting superior accuracy. This research contributes to understanding the capabilities and limitations of LLMs in processing and interpreting complex financial data from corporate documents.

The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.

We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset. The VEDB's potential applications are vast, including improving gaze-tracking methodologies, assessing spatiotemporal image statistics, and refining deep neural networks for scene and activity recognition. The VEDB is accessible through established open science platforms and is intended to be a living dataset with plans for expansion and community contributions. It is released with an emphasis on ethical considerations, such as participant privacy and the mitigation of potential biases. By providing a dataset grounded in real-world experiences and accompanied by extensive metadata and supporting code, the authors invite the research community to use and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings.

Neural Dynamics Associated with Biological Variation in Normal Human Brain Regions.

The processes involved in encoding and decoding signals in the human brain are a continually studied topic, as neuronal information flow involves complex nonlinear dynamics. This study examines awake human intracranial electroencephalography (iEEG) data from normal brain regions to explore how biological sex influences these dynamics. The iEEG data were analyzed using permutation entropy and statistical complexity in the time domain and power spectrum calculations in the frequency domain. The Bandt and Pompe method was used to assess time series causality by associating probability distributions based on ordinal patterns with the signals. Due to the invasive nature of data acquisition, the study encountered limitations such as small sample sizes and potential sources of error. Nevertheless, the high spatial resolution of iEEG allows detailed analysis and comparison of specific brain regions. The results reveal differences between sexes in brain regions, observed through power spectrum, entropy, and complexity analyses. Significant differences were found in the left supramarginal gyrus, posterior cingulate, supplementary motor cortex, middle temporal gyrus, and right superior temporal gyrus. This study emphasizes the importance of considering sex as a biological variable in brain dynamics research, which is essential for improving the diagnosis and treatment of neurological and psychiatric disorders.

Importance of the quality management of aerobiological monitoring networks: The case study of Madrid Region in Spain

Interest in biological air quality monitoring is rising, and updated public information is increasingly demanded by stakeholders in the case of airborne pollen, which requires maintaining high standards of data quality. The number of aerobiological stations worldwide is continuously growing, and quality management is becoming more complex with the increase in the scale of aerobiological networks. Quality control exercises are crucial for maintaining the quality of the data used in the pollen monitoring routine over time. In this study we show the results of an intercomparison test among technicians in the Madrid Region Palynological Network in central Spain in order to identify potential sources of error during the pollen analysis. The findings of this intercomparison exercise indicated very high-quality pollen data based on two different proficiency tests: i) the technicians' analysis of common samples with the light microscopes used in the routine pollen analysis; and ii) the analysis of common samples with all technicians using the same light microscope. A few specific remarkable errors were detected (i.e., those whose Absolute Error > |10| and Relative Error > |20 %|), such as confusion by four participants between Quercus vs. Acer and Quercus vs. Platanus pollen. Since the pollen analysis routine requires very experienced professionals, we believe it is essential to provide adequate training programmes for pollen analysis and the specific identification of these problematic pollen types. High-quality environmental data is crucial for sharing with regional, national and continental networks to ensure applicability, reproducibility and integration in large-scale studies.

Quality control of 3D printing in bioarchaeology: a case study on dimensional assessment of cranial models

In the last few years, 3D printing has gained widespread use in archaeology and cultural heritage fields, from research and conservation to enriching museum experiences. This study focuses on Fused Deposition Modeling (FDM) technology to assess the quality of printed replicas of archaeological human remains. A cranial model was 3D printed (3DP) from Computed Tomography (CT) data of an 8-year-old patient to simulate the remains of an archaeological skull. Eight copies were then printed and subjected to CT scanning to compare them to the original model through an objective measurement method based on image analysis. The proposed method investigates print variability and considers potential sources of error to assess the dimensional compatibility of the model before and after printing. Results showed an increasing error, up to 15 %, with higher levels of model detail. These results are discussed with reference to a metrological approach, highlighting the need for further research into optimizing 3D printing quality control, including through the definition of a standardized protocol to obtain archaeological replicas faithful to the originals.

Optical actinometry for number density measurements in low-pressure plasmas: Advantages, error sources, and method validation

Number density of plasma-generated atoms or molecules is an important parameter for both fundamental research and applications. It can be measured in a straightforward manner, using vacuum-ultraviolet absorption spectroscopy, which is mainly possible in laboratory conditions as it may require bulky equipment, such as lasers. By contrast, optical actinometry is an alternative approach that only uses spontaneous emission from the plasma. This technique relies on the so-called corona excitation and uses emission line ratios between the gases with unknown and known concentrations (called actinometer in the last case). As a result of using line ratios, the additional density calibration is not required if the excitation cross sections are known. This study discusses Ar-based actinometry in low-pressure (roughly <1 kPa) plasma discharges with an emphasis on multiple line ratios. The work is particularly focused on the method’s applicability, the choice of Ar cross sections, and potential error sources. The influence of the additional excitation mechanisms is analyzed based on both experiments and modeling. The optical transitions for F, O, H, N, and P atoms along with expressions for their number density are presented, not requiring high optical resolution for measurements. For the sake of method validation, it is shown that in low-pressure radiofrequency discharges, a nearly excellent agreement between the actinometry data and the calibrated measurements can be achieved by careful selection of optical transitions.

Quantitative analysis of spectral properties and composition of primitive achondrites (acapulcoites, lodranites and winonaites)

The establishment of robust meteorite-asteroid links has been a major focus of planetary exploration, and a major driver of asteroid sample return missions. Reflectance spectroscopy has been shown to be a powerful tool for this purpose. For the meteorites dominated by silicate minerals, quantitative analysis of spectral absorption features caused by the Fe2+-bearing minerals (mainly olivine and pyroxene) is a common method to determine mafic silicate mineralogy and end member abundances, and establish the relationship between them and possible parent bodies. In this study, the reflectance spectra of 22 primitive achondrites (acapulcoites, lodranites and winonaites) from NASA RELAB database were analyzed to determine their positions in the plot of the band area ratio (BAR) and 1 μm band center (Band I center). We found that Band I center and BAR of acapulcoites and lodranites are in roughly the same range. Acapulcoite-lodranite partially overlap with the field of H chondrites in the plot of the BAR and Band I center. This overlap means that spectral calibrations (also referred to as mineralogical formulas) based on the two types of meteorites needs to be applied with caution. The 2 μm band center of acapulcoite–lodranite is significantly lower than that of H chondrites, which is consistent with the conclusion of previous studies and provides a means to separate these two groups. In addition, the choice of spectral parameter analysis techniques may be a potential error source in similar studies. We provide generalized spectral fields of primitive achondrites in the plot of the BAR and Band I center derived from two widely used technologies.

Why Students Cannot Easily Integrate Component Skills: An Investigation of the Composition Effect in Programming

Programming skills are increasingly important to the current digital economy, yet these skills have long been regarded as challenging to acquire. A central challenge in learning programming skills involves the simultaneous use of multiple component skills. This article investigates why students struggle with integrating component skills—a challenge called the composition effect. This effect was first studied in learning mathematics, and findings in that area have led to more effective instruction in mathematics. In the current work, we systematically investigate the nature of the composition effect in novice code tracing through a classroom study in a university-level introductory programming course. We designed a set of problems that require integrating component skills, along with matched problems that use the same component skills separately. The composition effect was defined as the performance difference between these two types of problems. We further singled out errors that were committed in integration problems but not in the matched problems and conducted qualitative coding to categorize these errors. Statistical analyses produced three main findings. First, we found a robust composition effect across problems and students, mostly with small or medium effect sizes. Second, we identified two potential sources of errors for this composition effect: conceptual misunderstandings of how to integrate component skills and process errors resulting from slips related to cognitive load challenges. In particular, additional conceptual knowledge often appears to be needed when component skills are integrated in specific ways. Moreover, the primary source appears to be misunderstandings of how skills integrate. Composition effects may not generalize across topics, which suggests that domain topics may serve as the conceptual basis for the composition effect. Third, we found stable individual differences in susceptibility to the composition effect in both overall and finer-grained levels. Our findings provide implications for building theories that may generally explain challenges in learning complex skills and may result in practical designs for skill integration enhanced curricula and instruction to foster student mastery in computing education.

An effective drift-diffusion model for pandemic propagation and uncertainty prediction

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. In this paper, the canonical susceptible-infected-recovered model is simplified, in the process generating quasi-analytical solutions and fitting functions that agree well with the numerics, as well as infection data across multiple countries. The equations provide an elegant way to visualize the evolution of the pandemic spread, by drawing equivalents with the similar dynamics of a particle, whose location over time represents the growing fraction of the population that is infected. This particle slides down a potential whose shape is set by model epidemiological parameters such as reproduction rate. Potential sources of errors and their growth over time are identified, and the uncertainties are mapped into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model sets the foundation for their eventual extension to a multi-patch model while offering practical error bounds and could thus be useful in making policy decisions going forward.

Performance of pulse oximeters as a function of race compared to skin pigmentation: a single center retrospective study.

Pulse oximetry (SpO2) is a critical monitor for assessing oxygenation status and guiding therapy in critically ill patients. Race has been identified as a potential source of SpO2 error, with consequent bias and inequities in healthcare. This study was designed to evaluate the incidence of occult hypoxemia and accuracy of pulse oximetry associated with the Massey-Martin scale and characterize the relationship between Massey scores and self-identified race. This retrospective single institute study utilized the Massey-Martin scale as a quantitative assessment of skin pigmentation. These values were recorded peri-operatively in patients enrolled in unrelated clinical trials. The electronic medical record was utilized to obtain demographics, arterial blood gas values, and time matched SpO2 values for each PaO2 ≤ 125 mmHg recorded throughout their hospitalizations. Differences between SaO2 and SpO2 were compared as a function of both Massey score and self-reported race. 4030 paired SaO2-SpO2 values were available from 579 patients. The average error (SaO2-SpO2) ± SD was 0.23 ± 2.6%. Statistically significant differences were observed within Massey scores and among races, with average errors that ranged from - 0.39 ± 2.3 to 0.53 ± 2.5 and - 0.55 ± 2.1 to 0.37 ± 2.7, respectively. Skin color varied widely within each self-identified race category. There was no clinically significant association between error rates and Massey-Martin scale grades and no clinically significant difference in accuracy observed between self-reported Black and White patients. In addition, self-reported race is not an appropriate surrogate for skin color.

Biological reliability of a movement analysis assessment using a markerless motion capture system.

Advances in motion capture technology include markerless systems to facilitate valid data collection. Recently, the technological reliability of this technology has been reported for human movement assessments. To further understand sources of potential error, biological reliability must also be determined. The aim of this study was to determine the day-to-day reliability for a three-dimensional markerless motion capture (MMC) system to quantify 4 movement analysis composite scores, and 81 kinematic variables. Twenty-two healthy men (n = 11; ; age = 23.0 ± 2.6 years, height = 180.4.8 cm, weight = 80.4 ± 7.3 kg) and women (n = 11; age = 20.8 ± 1.1 years, height = 172.2 ± 7.4 cm, weight = 68.0 ± 7.3 kg) participated in this study. All subjects performed 4 standardized test batteries consisting of 14 different movements on four separate days. A three-dimensional MMC system (DARI Motion, Lenexa, KS) using 8 cameras surrounding the testing area was used to quantify movement characteristics. 1 × 4 RMANOVAs were used to determine significant differences across days for the composite movement analysis scores, and RM-MANOVAs were used to determine test day differences for the kinematic data (p < 0.05). Intraclass correlation coefficients (ICCs) were reported for all variables to determine test reliability. To determine biological variability, mean absolute differences from previously reported technological variability data were subtracted from the total variability data from the present study. No differences were observed for any composite score (i.e., athleticism, explosiveness, quality, readiness; or any of the 81 kinematic variables. Furthermore, 84 of 85 measured variables exhibited good to excellent ICCs (0.61-0.99). When compared to previously reported technological variability data, 62.3% of item variability was due to biological variability, with 66 of 85 variables exhibiting biological variability as the primary source of error (i.e., >50% total variability). Combined, these findings effectively add to the body of literature suggesting sufficient reliability for MMC solutions in capturing kinematic features of human movement.

Lumbar Spine Densitometry in People with Spinal Cord Injury: Investigation of Potential Sources of Errors

Purpose: People with spinal cord injury (SCI) experience a considerable loss of bone after the injury. Lumbar spine (LS) bone mineral density (BMD) has been reported to be within the normal range, or even higher when assessed with DXA, in people with SCI; hence, it has been hypothesized that sources of error may spuriously increase LS BMD. The aim of this study was to describe the frequency of potential sources of error that may alter LS BMD measurement in a cohort of individuals with chronic SCI at baseline and over a 2-year period. Methods: We analyzed baseline and 2-year follow up DXA scans (Hologic Discovery QDR 4500, Hologic Inc., MA, USA) previously performed from a cohort of males and females with chronic SCI. Two physicians independently reviewed each scan, commented on whether the scan was appropriate for BMD analysis, should be re-analyzed, or be removed from the dataset, and reported on the presence of potential sources of error in LS BMD measurement. Results: We reviewed 115 lumbar spine DXA scans from 58 participants, and 107 (93.0 %) scans from 52 participants presented at least one potential source of error. At baseline, the average number of potential sources of error per scan was 5.5 ± 1.7 and 5.7 ± 1.5 according to rater 1 and rater 2, respectively. Follow-up scans presented an average of 5.6 ± 1.6 and 5.7 ± 1.4 potential sources of error according to rater 1 and rater 2, respectively. Facet sclerosis, osteophytes and difficulty in detecting bone edges were the most prevalent sources of error. Conclusion: The high frequency of potential sources of error is consistent with current recommendations against the use of LS BMD for fracture risk assessment in people with SCI.